Abstract

Bionic anti-reflection windows are critical for enhancing the performance of aerospace infrared detection systems. The manufacturing of anti-reflective microstructures (ARMs), however, faces a significant challenge that the transmittance spectrum is difficult to predict both accurately and swiftly, leading to long-term reliance on blind and inefficient trial-and-error for process optimization. Here, we report a method that integrates machine learning (ML) with femtosecond laser for the rapid customization of high-performance anti-reflection windows.

Embedding of the material’s absorption characteristics as a physical constraint into the ML model enables highly accurate prediction across an ultra-broad transmittance spectrum, overcoming the failure of conventional simulations in these intrinsic absorption bands. The trained ML model serves as an intelligent agent to guide the precise control over multiple femtosecond laser parameters, thus converting the costly process of physical trial-and-error into one of efficient virtual screening and iteration.

As a proof of concept, an anti-reflective sapphire window was produced that demonstrates broadband (3.3–6.0 μm) and high transmittance (~96.8% peak at 4.2 μm), along with excellent wide-angle characteristics, mechanical wear resistance, and high-quality imaging capability. This work provides a novel paradigm for rapidly manufacturing high-performance anti-reflective windows, laying the foundation for next-generation optical components.